Eigenface Based Recognition of Emotion Variant Faces

Thuseethan, S., Kuhanesan, S.


In present, the automatic machine based face recognition has become significant due to its urgency in potential application and current scientific challenges of industries. However, most of the existing systems designed up to now can only effectively distinguish the faces when source images are collected under numerous constrained conditions. The success rate or positive impact of face recognition systems depend on a variety of information imposed in images of human faces. Pose of face, facial expression, angle, occlusion and state of structural components are some of those. Emotions can be expressed in different ways that can be seen such as facial expression, speech, written text and gestures. This model propose an efficient approach for the recognition of expression or emotion variant faces since there are very few emotion recognition software tools to handle such problems and there is a significant importance to this research area in the field of face recognition. Especially an approach proposed here to face recognition where the facial expression in the training image set and in the testing image set diverge and only one sample image per class is existing in the system. The input to the system is a frontal neutral expression oriented face image with unique background. In this image the hair is tied away from the face and facial hair should be removed. Principal Component Analysis approach was used as a primary mechanism in the proposed model. This approach has been applied purely on a set of face images in order to extract a set of eigenface images as the output. Here weights of the representation or image are used for recognition of emotions. One of the distance metric approaches Euclidean Distance used to discover the distance with the weight vectors which was associated with each of the training images for the existence of classification task.

Keywords: Face Recognition, Emotion-variant faces, Image Processing, Principal Component Analysis, Euclidean Distance

Full Text: PDF
Download the IISTE publication guideline!

To list your conference here. Please contact the administrator of this platform.

Paper submission email: CEIS@iiste.org

ISSN (Paper)2222-1727 ISSN (Online)2222-2863

Please add our address "contact@iiste.org" into your email contact list.

This journal follows ISO 9001 management standard and licensed under a Creative Commons Attribution 3.0 License.

Copyright © www.iiste.org